Banking automation using autonomous robot

ABSTRACT

A banking automation system comprising a robot for robotically assisting a plurality of individuals in retrieval and storage of goods deposited in a bank, wherein during said retrieval and storage of goods by the robot: (a) each of the plurality of the individuals maintains privacy by being placed in a private location, (b) the plurality of the individuals are able to retrieve and store goods simultaneously, and (c) identification of each of the plurality of the individuals is verified biometrically.

RELATED APPLICATIONS

This application claims benefit to U.S. Provisional Application No.61/528,566, entitled “QUANTITATIVE ASSESSMENT OF ROBOT MAPPING FROM SLAMTO DEAD RECKONING AND IMPROVEMENTS THEREOF,” filed on Aug. 29, 2011,which is incorporated herein in its entirety.

All publications, patents, and patent applications cited in thisspecification are hereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to a method andsystem for providing an improved approach for automating the bankingsector using robotics and artificial intelligence.

BACKGROUND OF THE INVENTION

A robot is a mechanical or virtual intelligent agent that can performtasks automatically or with guidance, typically by remote control.Autonomous robots are robots that can perform desired tasks instructured or unstructured environments without continuous humanguidance. In today's world, robots may be used in many sectors (orapplications) such as medical sector, banking sector, transportationsector, military sector, etc. More particularly, robots may be used toautomate the banking sector. For example, robots may be configured in away that it can go to the safe deposit vault without requiring any humanintervention. The robot may be given a set of secret keys (by its owner)for security purposes. Further, robots may be used in bankingsurveillance and monitoring.

Robots need to be able to create a map using map-making algorithms thataccurately displays their environment to be effective in theirapplication. Maps display what robots see, and therefore it is vitalthat these maps are as accurate as possible because they lead to morepractical applications of these types of robots. Currently availabletechnologies use dead reckoning as a mapping method in navigation ofrobots.

Dead reckoning calculates the current position of the robot by using apreviously determined position. With mobile robots, the position isdetermined by rotary wheel encoders. The main problem that arises with atwo wheel drive train is that the robot's wheels tend to frequently slipor skid, especially during turning, causing the encoders to returnlarger values than what they should. When encoder data is mapped, it isquite inaccurate, as no measures are taken to properly correct the slipsand skids that have occurred.

Therefore, what is needed is an accurate location service which may beapplied to automating the banking sector and which overcomes thelimitations exhibited by current banking methods.

SUMMARY

An embodiment relates to a method for conducting banking automationcomprising robotically assisting a plurality of customers using a robotin retrieval and storage of goods deposited in a bank, wherein duringsaid retrieval and storage of goods by the robot: (a) each of theplurality of the customers maintains privacy by being placed in aprivate location, (b) the plurality of the customers are able toretrieve and store goods simultaneously, and (c) identification of eachof the plurality of the customers is verified with either one or acombination of account number, smartcard, biometric data, passwords,physical keys, etc. Another embodiment relates to a banking automationsystem comprising a robot for robotically assisting a plurality ofcustomers in retrieval and storage of goods deposited in a bank. Yetanother embodiment relates to a tangible non-transitory computerreadable medium comprising computer executable instructions that, whenexecuted by one or more processors, cause the one or more processors toconduct banking automation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1—Layout of the bank, vault, and consumer access rooms.

FIG. 2—Robot interactions.

FIG. 3—(a) Strategy to map environment for robot; (b) Overarchinghierarchy of consumer-robot interactions.

FIG. 4—Retrieval of goods by the robot.

FIG. 5—Robot security architecture and biometric identification.

DETAILED DESCRIPTION

Simultaneous localization and mapping (SLAM) is a technique used byrobots and autonomous vehicles to build up a map within an unknownenvironment (without a priori knowledge), or to update a map within aknown environment (with a priori knowledge from a given map), while atthe same time keeping track of their current location.

Maps are used to determine a location within an environment and todepict an environment for planning and navigation; they support theassessment of actual location by recording information obtained from aform of perception and comparing it to a current set of perceptions. Thebenefit of a map in aiding the assessment of a location increases as theprecision and quality of the current perceptions decrease. Mapsgenerally represent the state at the time that the map is drawn; this isnot necessarily consistent with the state of the environment at the timethe map is used.

The complexity of the technical processes of locating and mapping underconditions of errors and noise do not allow for a coherent solution ofboth tasks. Simultaneous localization and mapping (SLAM) is a conceptthat binds these processes in a loop and therefore supports thecontinuity of both aspects in separated processes; iterative feedbackfrom one process to the other enhances the results of both consecutivesteps.

Mapping is the problem of integrating the information gathered by a setof sensors into a consistent model and depicting that information as agiven representation. It can be described by the first characteristicquestion, What does the world look like? Central aspects in mapping arethe representation of the environment and the interpretation of sensordata.

In contrast to this, localization is the problem of estimating the place(and pose) of the robot relative to a map; in other words, the robot hasto answer the second characteristic question, Where am I? Typically,solutions comprise tracking, where the initial place of the robot isknown, and global localization, in which no or just some a prioriknowledge of the environmental characteristics of the starting positionis given.

SLAM is therefore defined as the problem of building a model leading toa new map, or repetitively improving an existing map, while at the sametime localizing the robot within that map. In practice, the answers tothe two characteristic questions cannot be delivered independently ofeach other.

Before a robot can contribute to answering the question of what theenvironment looks like, given a set of observations, it needs to knowe.g.: the robot's own kinematics; which qualities the autonomousacquisition of information has, and; from which sources additionalsupporting observations have been made. It is a complex task to estimatethe robot's current location without a map or without a directionalreference. Here, the location is just the position of the robot or mightinclude, as well, its orientation.

SLAM can be thought of as a chicken or egg problem: An unbiased map isneeded for localization while an accurate pose estimate is needed tobuild that map. This is the starting condition for iterativemathematical solution strategies.

The answering of the two characteristic questions is not asstraightforward as it might sound due to inherent uncertainties indiscerning the robot's relative movement from its various sensors.Generally, due to the budget of noise in a technical environment, SLAMis not served with just compact solutions, but with a bunch of physicalconcepts contributing to results. If at the next iteration of mapbuilding the measured distance and direction traveled has a budget ofinaccuracies, driven by limited inherent precision of sensors andadditional ambient noise, then any features being added to the map willcontain corresponding errors. Over time and motion, locating and mappingerrors build cumulatively, grossly distorting the map and therefore therobot's ability to determine (know) its actual location and heading withsufficient accuracy.

Different aspects of SLAM include the following:

(1) Mapping, i.e., the process of creating geometrically consistent mapsof the environment.

(2) Sensing, i.e., using several different types of sensors to acquiredata with statistically independent errors. The sensors could be opticalsensors that may be one dimensional (single beam) or 2D-(sweeping) laserrangefinders, 3D Flash LIDAR, 2D or 3D sonar sensors and one or more 2Dcameras. For VSLAM (visual SLAM), the sensors could be visual (camera)sensors. The sensors could also be quasi-optical wireless sensorsranging for multi-lateration (RTLS) or multi-angulation in conjunctionwith SLAM. A special kind of SLAM for human pedestrians could use a shoemounted inertial measurement unit as the main sensor and relies on thefact that pedestrians are able to avoid walls. This approach calledFootSLAM can be used to automatically build floor plans of buildingsthat can then be used by an indoor positioning system.(3) Locating: The results from sensing could be fed to the algorithmsfor locating. According to propositions of geometry, any sensing mustinclude at least one lateration (a navigation technique based on themeasurement of the difference in distance to two or more stations atknown locations that broadcast signals at known times) and (n+1)determining equations for an n-dimensional problem. In addition, theremust be some additional a priori knowledge about orienting the resultsversus absolute or relative systems of coordinates with rotation andmirroring.(4) Modeling: Contribution to mapping may work in 2D modeling andrespective representation or in 3D modeling and 2D projectiverepresentation as well. As a part of the model, the kinematics of therobot could be included, to improve estimates of sensing underconditions of inherent and ambient noise. The dynamic model balances thecontributions from various sensors, various partial error models andfinally comprises in a sharp virtual depiction as a map with thelocation and heading of the robot as some cloud of probability. Mappingis the final depicting of such model, the map is either such depictionor the abstract term for the model.

The term “bank” refers to any depository that safeguards properties. Forexample, the bank could be a financial institution that safeguardsvaluables and/or money of customers. The bank could be a tissue bank,such as for a good laboratory practice (GLP) or non-GLP setting. Thebank could be a gene bank. The bank could be a library for storage ofbooks. The bank could be a central intelligence agency that safeguardsclassified or non-classified data and goods. The bank could be anarchive or an armory for storage of weapons.

An embodiment relates to a banking automation by autonomous robotapplying, for example, three process steps of (1) mapping the bank areawith a robot; (2) retrieval of goods from a certain area such as lockersin a vault; and (3) storage of goods to a certain area such as lockersin a vault. These three steps are described in further details as below:

Mapping Bank Area with a Robot

-   -   a. Generating its own map        -   i. Or having a map inputted by the user    -   b. Map includes locker numbers, which correspond with the        numbers on the locker owners' keys        Retrieval    -   a. People come into bank, which is built specially for automated        banking, and enter one of the customer rooms, for example, as        shown in FIG. 1, reference numeral 3.    -   b. Locker owner gives robot (FIG. 1, reference numeral 4) an        electronic key, which is marked with a code that informs the        robot which locker to go to in its internal map, through small        opening.    -   c. The key also contains information about the owner for        security. If there is a thief who finds a random key, the        biometric information (fingerprints, photograph, signatures,        etc.) could not match and the police can be phoned right away.    -   d. The bank's manager could also have a key for each locker, and        make sure that the key the owner has is authentic and matches        the one of the manager.    -   e. When all security aspects are finished, the robot goes to the        respective locker (FIG. 1, reference numeral 1) and retrieves        the box inside that contains the client's belongings by scanning        or inserting the key that was given. On the other hand, the        owner may be allowed to have a touch screen from which he/she        can choose exactly what they would want from their box.        Storage    -   a. After the owner has finished picking out his/her possessions,        the robot returns the box to the correct locker.    -   b. Finally, the robot returns the key to the owner    -   c. Since there could be various robots in the room that can help        various owners, the robots need to be able to avoid each other        effectively    -   d. The robots do all this in a dark vault (FIG. 1, reference        numeral 2), so people cannot see through the small openings in        the rooms. Also, they could have extendable arms to reach for        higher lockers

Unlike today's banks, this automated bank using intelligent mobilerobots could give multiple people access to their lockers with increaseprivacy and security. The rooms that each individual is in could belocked, preventing the chance of a robbery. There could be multipleidentity checks done by the robot, and this process could be fast andefficient due to the fact that there are multiple robots.

An embodiment relates to the actual architecture of the banking area. Asshown in an exemplary schematic in FIG. 1, clients can enter the bankand choose one of the multiple rooms. Each of these rooms is secure andcan be locked from the inside. These smaller rooms are there so thatclients can privately and safely give their keys and biometricinformation to the autonomous robots without others watching. Itprotects clients from being attacked by thieves and allows them toprivately take out their belongings. In addition, the vault where thevarious lockers could be contained could be dark without any lights.This could prevent clients from looking into the vault and seeing whichlockers are being opened by robots to insure security. Because of this,the robots could be able to move around in darkness and need not rely onthe light to move around. Another advantage of having multiple rooms isthat multiple people can access their belongings and make use of therobots simultaneously. Of course, the number of rooms could varyaccording to the area given for the bank. In today's banks, only oneperson can access their vault or box at a time to insure safety. Withthis new bank, clients can get their belongings much faster in a moreefficient manner.

While optionally there could be different number of robots than thenumber of customer rooms, in another embodiment there could be the samenumber of robots as there are customer rooms. This way, multiple clientscan make use of the robots to get their boxes at the same time. Althoughit may be expensive to have so many robots, it could make a moreefficient bank and end up creating greater income. The extensivesecurity checks done by the robots could also encourage more clients tocome to this bank over another. As shown in FIG. 2, the robots(reference numeral 2) interact specifically with four differentitems/people. First, the robot interacts directly with the client byaccepting the key given (FIG. 2, reference numeral 1). The robot thencan authenticate a person based on one or more biometrics (fingerprints,facial image, Iris scan, Voice print, or on client's signature, etc)and/or a smartcard. The robot also interacts with the key (FIG. 2,reference numeral 3) by reading all of the pre-inserted biometric datain it. Third, if the data read from the key and the data that is createdby the robot when investigating the client don't match, the robotinteracts with the bank manager or police (FIG. 2, reference numeral 4),making sure that if a thief is trying to use another person's card,he/she could get caught. Finally, if all the security checks arecompleted successfully, the robot could use the locker informationstored in the card to locate the vault or locker (FIG. 2, referencenumeral 5) that it corresponds to in its internal map. The intelligent,autonomous robots could bring a large improvement in the banking system.

FIGS. 3 a and 3 b are exemplary schematics of an overarching hierarchyfor the customer (e.g., an owner of the safe deposit box) accessing thesafe deposit box. The customer could enter a customer room with lockingcapabilities. While the customer is inside the customer room, the robotwould take a picture of the customer, biometric data of the customer, aswell as his or her key to his or her safe deposit box. If the biometricinformation process is fulfilled, as explained in the subsequentparagraph, the robot enters the darkened vault and proceeds to therespective locker using Simultaneous Localization and Mapping (SLAM), aprogram that will be explained further below. After arriving at thelocker, the robot will realign itself so that it can access the box fromthe locker and carry it to the customer. As the customer finishespicking specific belongings, the robot will return the box to the lockerand await another customer.

Using a database, the robot would confirm that the key is for their bankand the person in the room is the owner of the key. The picture would beadded to the database for reference. If the data does not match up, thedoor would remain locked, the picture would be added to a blacklist forthe bank, the authorities would be alerted, and the key would be sent tothe owner. If the data is correct, the robot would then leave the room,and enter the sealed safety deposit box room through a small exit. Thisroom would be kept dark, so that only robots would be able to navigatethrough. While driving towards its target, the robot would be using SLAMto fix its odometry to prevent errors caused by sliding and slipping.But as discussed later, SLAM can still return faulty data, so the robothas the option of checking the nearby barcodes to validate its position.Once the robot has arrived, it uses the safety deposit box to alignitself with the box, and then uses (by means of scanning, entering,etc.) both the customer's key and the robot's key to attempt to open thebox. If the box does not open, the robot checks to see if it is in theright location. If the robot is in the right location but the keys stilldon't work, the robot takes the key back to the customer so that theycan talk to bank management for a replacement key. If the location wasincorrect, the robot could then use the before mentioned method to driveto the correct location. If the keys did open the safety deposit box,then the robot could take out the inner box, and by using the beforemention driving method return the customer's room. The robot could waitfor the customer to finish. Then the robot could then take both theinner box and the key back to the safety deposit box location. After theinner box is returned, the robot could take the key back to the user,and wait for a new customer.

FIG. 4 is schematic of the retrieval process carried out by the robot.The robot scans the customer's key card, matches it up with the correctlocker that corresponds to the customer's key card, and decides on apath to reach the correct locker. To reach the correct locker, the robotneeds to find the shortest path to the locker after accessing it fromits internal map. To actually do this, the robot could be usingSimultaneous Localization and Mapping (SLAM). The Robot OperatingSystem, or ROS (a robot software geared towards Linux systems for opensource collaboration of robotic software), contains multiple SLAM, orSimultaneous Localization and Mapping, implementations. After doing someexperiments, data that was presented showed that Gmapping, animplementation of SLAM, is far more accurate than other mappingalgorithms such as Dead Reckoning, a mapping method in navigation thatcalculates current position by using a previously determined position.With mobile robots, the position is determined by rotary wheel encoders.The main problem that arises with a two wheel drive train is that therobot's wheels tend to frequently slip or skid, especially duringturning, causing the encoders to return larger values than what theyshould. When encoder data is mapped, it is quite inaccurate, as nomeasures are taken to properly correct the slips and skids that haveoccurred. Using dead reckoning can lead to disasters in the bank, asrobots may go to wrong lockers by mistake. On the other hand, SLAMbuilds a map whilst in an unknown environment and keeps track of thecurrent position. Rotary encoders are used to keep track of the currentlocation and laser scans from a laser measurement technology device madeby SICK are used to correct the map when the wheels slip or skid. Thelaser measurement technology device can be used for a wide range ofapplications. The device, which detects both 2D and multi-dimensionalcontour data, can process information either externally or in the sensoritself. Since the encoders and the laser measurement device are workingin unison, they are configured to create a more accurate map. SLAM wasproven to be far more accurate after our experimentation.

The biometric scanning that could take place could be similar to thatexplained in U.S. Pat. No. 7,970,678, which is incorporated herein inits entirety by reference, discloses several biometric financialtransaction systems and methods. For example, consumers could accesstheir goods located in a safe deposit vault after validation andverification of the customer by biometrics as shown in FIG. 5.Similarly, there is at least one robot that is able to fulfill ordersfrom the consumer. A biometric or a biometric sample is any unique humancharacteristic of which a scan or image is taken directly from theperson. The biometric or biometric sample may be, but is not limited to,any of the following: a fingerprint, a retinal image, an iris image, afacial image, and voice print.

A robot provides the ability to accept biometric and other data asinput, to identify the consumer from this data, and to either completeitem retrieval. Communication links exist or can be established betweenthe robot and the database. In one embodiment, one could use a proxy toretrieve goods from the bank vault while the owner of the goods in thevault could be remote, for example in a hospital. In this case, therecould be double authentication of both the proxy and the owner. Acommunication link with the remote owner can be a permanent connection(e.g., a leased line), a temporary switched-circuit connection (e.g., adialup telephone call), or a virtual connection (e.g., via packetswitching). Encryption can be employed on all communication links toprotect sensitive data, as is standard in the industry.

The robot may or may not be required to contain in memory any data whichis personalized to or unique to the consumer in order for the consumerretrieve or deposit goods. Additionally, it can be used as a biometricinput device using the consumer's voice as a biometric. A wireless orcellular telephone with a built-in finger image scanner or otherbiometric sensor can also be used. This is like the example above, butuses a biometric other than voice, e.g., a finger image. In addition,the consumer can use a wireless personal digital assistant (PDA) such asa smart phone, with a microphone or other biometric sensor. The wirelessPDA can be used to enter and communicate an order to a robot, and amicrophone or other biometric sensor can be used to input a biometric.Other access devices could be apparent to those of ordinary skill in theart.

Every key possesses an identification (ID) code. This ID code is uniqueto the locker. Examples of ID codes include a digital certificate thatincludes the number of the locker, ID of the consumer in the data base,etc.

A database can be a single computer that or a large collection ofcomputers that serves a number of different robots. The database acceptsqueries of biometric data and identifies consumers from this data. Onceidentified, the robot retrieves goods from the consumer's locker. Thisinformation could be recorded.

In another embodiment, a wireless PDA is the access device used. Asdiscussed above, there could be different biometrics. For illustrativepurposes, a voice biometric could be used in this embodiment. Use of thesystem in this embodiment proceeds as follows: (1) A consumer uses theaccess device (wireless PDA) to contact the robot or bank. (2) The robotreceives the ID code from the key. In this embodiment, this could bedigital certificate identification or a number stored in the device. Asdescribed above, a voice biometric is used for illustrative purposes inthis embodiment, but other biometric identifiers are possible. If theconsumer cannot be identified after repeated tries, the robot alerts ahuman customer service assistant, or security personnel, who can useother means to identify the consumer. (3) In the event of a successfulidentification, the robot retrieves goods for the consumer.

There are many advantages of having biometric identification. First,because each transaction is authorized using a biometric received fromthe consumer's person, the transaction cannot be repudiated. Second, theembodiments are convenient for the consumer, particularly locatedremotely from the robot, for example an elderly person in a hospital, inthat the robot handles all account information, eliminating the need torecite or otherwise enter credit card or other account numbers into atelephone or PDA. Third, the use of biometrics and encryption providessecurity, eliminating the possibility of fraud via interceptingtransmissions from the telephone or PDA. Fourth, the system supports theuse of multiple consumers to access the vault simultaneously in privacy,providing flexibility for the consumer.

Although the embodiments have been described with respect to aparticular biometric electronic transaction system and method for itsuse, it could be appreciated that various modifications of the systemand method are possible without departing from the invention.

All of the above mentioned ideas based on mapping using SLAM could bereplaced with a similar set-up with a radio, ultrasound, light, laser,infrared, other signals, or vision based localization method.

EXAMPLES

Materials and Hardware

This project used the P3DX robot from AdeptRobots, but other similarrobots could be analyzed the same way. On top of the robot must be somesort of distance sensor, which could be either a lidar or a depth sensorlike the Microsoft Kinect. For this project we used the LMS1XX lidarfrom SICK. An Ubuntu or other Linux based laptop is mounted to therobot, and could be running ROS. A joystick is needed for teleoperationof the robot. The robot should have the tires tested in between trials,to ensure that the pressure doesn't change and cause tire variationsacross tests.

Software

The laptop needs a recent version of ROS. This project used theDiamondback distribution, but can also be done with the Electricdistribution. The laptop could also need either a premade ROS wrapperstack (ROS packages) or a custom-made one. The same applies to the lidarsensor. In this project, the P2OS stack and LMS1xx stacks were used forthe robot and the laser respectively. We used Matlab as a basicvisualization tool for the Dead Reckoning map.

Methods

Mapping evaluation has so far been done by checking the map of a mappingalgorithm against the ground truth map of the area. This project splitsmapping into 5 main areas, each with their own separate procedure anddata values, which are described below.

The 90 Degrees Test:

In this test, while the laptop is recording the laser and odometry data,the robot passes by a 90 degree corner of a room from approximately 2 ftaway from the corner, going towards the right wall, turning and headingtowards the left wall. The data collected during the test is then set torun through Gmapping stack and displayed in ROS's visualization stack,RVIZ, and directly without preprocessing to Matlab to make theDead-Reckoning map. The maps are then checked for the angle of thecorner and the number of corners that are created.

The 90 Degrees Twice Test:

The procedure for this test starts the same as the previous test, butafter the robot finishes its movement, it turns toward right and goesforward, turns left and goes forward again, which leaves the robotextremely close the corner. The data is again sent to Gmapping and RVIZ,and Matlab, and the maps are checked for the angle of the corner and thenumber of corners that are created by the map.

The Straight Away Test:

In this test, the robot drives 430 cm. The recorded data is then runthrough Gmapping and RVIZ, and Matlab, and the generated map is thenchecked to see how far robot reported that it has moved. The distancecan be easily counted through the labeled axis, where in Matlab theunits are millimeters and in RVIZ the units are 0.5 meters.

The Circle Test:

In this test, the robot could move in a predetermined path that curvesaround obstacles, and then returns to the starting location. The data isthen made into maps, and the map is checked to see how far the endlocation is away from the start. There could always be some distancebecause the robot uses skid-turning, but the distances could be measuredand compared.

The Straight Wall Test:

In this test, the robot could start at the beginning of a long wall. Itcould then move in a semi random pattern of left and right that leadtowards the end of the wall, with the robot ending against the wall atthe very end. The data is then made into a map, and then checked to seehow straight the wall is. Since at the beginning and the end the robotis against the wall, a line drawn between the two is where the wallshould be, and then the wall visible is compared against the real wall.

Results

To ensure that all results were accurate, each of the five tests wasdone five times for both SLAM and Dead Reckoning.

The 90 Degree Test:

In the 90 Degrees Test, SLAM performed far better, as only one angle wasproduced on the map, and the average of the angles from the five trialswas 91.8°, which has only 2% error, as seen in Table 1. However, DeadReckoning produced two or three separate angles on the map, althoughthey were only 10 cm away from each other and appear as one when the mapis zoomed out. To deal with this problem, both or all three of theangles produced by Dead Reckoning in one trial were averaged to find afinal angle measurement of the trial. The average of those five finalmeasurements showed that Dead Reckoning displayed and angle ofapproximately 98.5°, which has 9.4% error. The standard deviation forthe SLAM trials was 0.84 degrees, while for the Dead Reckoning trials itwas 0.97 degrees, showing increased consistency in the trials done bySLAM on ROS. Table 1 is a data table of the test showing the angles fromboth SLAM and Dead Reckoning:

TABLE 1 Data from Dead Reckoning and SLAM of mapping a 90 degree corner.90 Degree Test Dead Reckoning SLAM Number Average Number Average ofAngle of of Angle of Trial No Angles Measures Angles Angles MeasuresAngles 1.0 3.0 101, 95, 97.6 1.0 92.0 92.0 97 2.0 3.0 94, 105, 99.3 1.093.0 93.0 99 3.0 2.0 98, 99 98.5 1.0 91.0 91.0 4.0 3.0 101, 92, 97.3 1.091.0 91.0 99 5.0 2.0 96, 103 99.5 1.0 92.0 92.0 Average 2.6 98.4 1.091.8 Standard 0.969 0.836 Deviation

The 90 Degree Twice Test:

The next test performed was the 90 Degrees Twice Test. This test wascontroversial, because 2 or 3 angles were created in each trial for bothSLAM and Dead Reckoning. So, Dead Reckoning created about the samenumber of angles it did in the 90 Degrees Test, and SLAM created manymore than the previous test because the area was scanned twice. SLAMshowed two completely separate walls, while Dead Reckoning showed threeangles near each other like the last test. However, SLAM was still moreaccurate in the angle of the wall than Dead Reckoning; the average ofall the angles in SLAM was 93.3°, which has an error of 3.7%, and inDead Reckoning the average was 110.6°, which has an error of 22.8%, asseen in Table 2. Although multiple angles were created in both, DeadReckoning was not able to measure the angle correctly. In DeadReckoning, the standard deviation was 2.05 degrees, while in SLAM it was0.63 degrees, again showing that results in SLAM are more precise. Table2 is a data table of the second test from both SLAM and Dead Reckoning:

TABLE 2 Dead Reckoning and SLAM mapping a 90 degree angle. 90 DegreesTwice Test Dead Reckoning SLAM Number Average Number Average of Angle ofof Angle of Trial No Angles Measures Angles Angles Measures Angles 1.03.0 112, 115, 112.3 2.0 93, 95 94.0 110 2.0 2.0 109, 111 110.0 2.0 94,93 93.5 3.0 3.0 108, 110, 109.6 3.0 94, 91, 92 92.3 111 4.0 3.0 107,108, 108.0 2.0 92, 94 93.0 109 5.0 2.0 112, 114 113.0 2.0 91, 96 93.5Average 2.6 110.6 2.2 93.3 Standard 2.048 0.630 Deviation

The Straight Away Test:

In the five Dead Reckoning trials, when the actual distance was 4.3meters, the average distance travelled by the robot was 4.58 meters, asseen in Table 3. The distance displayed on the Dead Reckoning map was onaverage 0.28 meters off the true distance. SLAM displayed a far moreaccurate distance, with an average of 4.18 meters. The ROS software wasoff by an average of 0.12 meters, less than half the inaccuracypresented by Dead Reckoning. The standard deviation of the five DeadReckoning trials was 0.125 meters, while the deviation for the SLAMtrials was only 0.1 meters. Table 3 is a data table for this StraightAway Test of the 4.3 meter run. The units on the Dead Reckoning map arein millimeters and the ROS map has squares that are each 0.5 meterssquared:

TABLE 3 Dead Reckoning and SLAM mapping while moving 4.3 meters.Straight Away Test Dead Reckoning SLAM Trial No Distance How Far OffDistance How Far Off 1.0 4.4 0.6 4.1 0.2 2.0 4.6 0.3 4.2 0.1 3.0 4.6 0.34.0 0.3 4.0 4.7 0.4 4.4 0.1 5.0 4.6 0.3 4.3 0.0 Average 4.6 0.3 4.2 0.1Standard Deviation 0.125 0.100

The Circle Test:

Circle Test also assessed the rotary encoder problem in the DeadReckoning software, as the P3-DX had to go around an obstacle in thecenter of the room and arrive back in the exact same spot. The initialand final locations of the robot should have been the same. However,because of the wheels' skidding on the robot, the Dead Reckoning mapshowed that the robot did not end in the same location. The average ofthe distances from the start on Dead Reckoning was 1.05 meters, whilethe average for SLAM was 0.58 meters, as seen in Table 4. This was onlytest where the standard deviation of SLAM, 0.28 meters, was greater thanthat of Dead Reckoning, being 0.17 meters.

TABLE 4 Dead Reckoning and SLAM mapping while traveling in a circle. Thecircle was then checked to see how far the robot was off in its endingpoint. Circle Test Dead Reckoning SLAM Trial No Distance From StartDistance From Start 1.0 0.9 0.6 2.0 1.1 0.5 3.0 1.2 0.2 4.0 0.9 1.0 5.01.2 0.6 Average 1.1 0.6 Standard Deviation 0.166 0.284

The Straight Wall Test:

The final test, the Straight Wall Test, was done to see whether or notDead Reckoning and SLAM could accurately locate a wall even if the P3-DXwas swaying wildly left and right. In this test, SLAM displayed a muchmore accurate map, as it only showed one wall, while Dead Reckoningshowed a map that did not present a definite location of the wall. Toevaluate this test, a line was placed where the wall actually shouldhave been on the map, and the various distances from the line weremeasured at set intervals. For each trial, the average of thesedistances was taken, as seen in Table 5. The average of all trials forDead Reckoning was 1.07 meters, which is large in comparison to the 0.19meters for SLAM. Dead Reckoning was also much less precise, because thestandard deviation of its trials was 0.31 meters, in comparison toSLAM's 0.04 meters. Table 5 is a data table for the test:

TABLE 5 Dead Reckoning and SLAM mapping a straight wall. Straight WallTest Dead Reckoning SLAM Average Distance Average Distance Trial No FromWall From Wall 1 1.26 0.17 2 1.13 0.21 3 0.96 0.14 4 0.60 0.19 5 1.400.23 Average 1.07 0.19 Standard Deviation 0.308 0.036Discussion

90 Degree Test:

The Dead Reckoning map had either two or three angles that formedcorners, but their separation was minuscule and only visible if thecorner was focused on. Of all the trials, the average was 98.4° with astandard deviation of 0.969 degrees. For the SLAM map, there was onlyone corner, and the average of all of its trials gets an average of91.8° with a standard deviation of 0.836 degrees. Since the DeadReckoning map had multiple walls, it can be concluded that the robot hadslipped twice, causing the two extra, but very close and similar, walls.The SLAM method, with its slipping correction, does not run into thesame problem with the multiple walls. SLAM also fixes the skidding whichhappens during the turning, making the angles more accurate, which inthis test was 466% more accurate from the formula 100*(Ø−Ødr)/(Ø−Ør),where Ø is 90°, Ødr is the angle from Dead Reckoning, and Ør is theangle from SLAM. Since the standard deviation for both methods aresmall, it shows that the values are consistent, which in the case ofDead Reckoning means consistently off, and in the case of SLAM meanconsistently accurate.

90 Degree Twice Test:

In this test, just like the prior one, the Dead Reckoning map had eithertwo or three walls, all very close and similar in angle. But with thistest the SLAM also has multiple walls, except that the walls areentirely separate, with a large distance between them. This happenedbecause of the way that SLAM works; SLAM tries to match points from whatit saw in the past to the current scans to try to fix the odometry skidsand slips. This encounters problems when the robot is in a rapidlychanging environment, such as a dynamic environment, or one which therobot is really close to a wall, where all small turns seem extreme. Theprogram then sometimes mismatches what the robot thinks is the samepoint, and then moves its location to match where it would be if thepoints actually did match. This causes the completely different wallswhen the laser data gets mapped from the new location. Even so, theaverage of all of the corners for SLAM is still close to 90 with anaverage of 93.26 with a standard deviation of 0.6304 degrees. DeadReckoning also got a worse than the previous test with more turns thatthe robot skids on, with an average of 101.6 with a standard deviationof 2.048 degrees. Using the formula from the last test, we get that SLAMis 355% better in its angle, but because of having separate walls unlikeDead Reckoning, which has very close walls together, it is not asaccurate of a map. Since the standard deviations for the data sets aresmall, it shows that the data is consistent.

The Straight Away Test:

In this test, the distance travelled is measured and then comparedrelative to what it should be, 4.3 meters. The reason this test wouldreveal different results from Dead Reckoning to SLAM is that with DeadReckoning, when the wheels slip, the robot thinks that it has movedforward while it had not actually moved during that time. With SLAM, itshould report something in the distance with the distance sensor, andadjust its location based on matching those objects to where it saw themin the past, making slip less effective in changing how far the robotthinks it has gone. For Dead Reckoning, it has an average of 0.28 metersmore than 4.3 meters with a standard deviation of 0.125 meters, and withSLAM, it has an average of 0.118 meters less than 4.3 meters with astandard deviation of 0.1001 meters. The SLAM algorithm is 237% betterthan Dead Reckoning, but both are really accurate numbers. For both DeadReckoning and SLAM have the standard deviation relatively high, meaningthat even though they are both relatively good numbers, it is notconsistent.

The Circle Test:

When the robot tries to turn, since it has two wheels, it skids to turn.This test tries to see if the robot, while it is skidding in the samedirection on a circular path, can keep its odometry accurate. With DeadReckoning, the average distance away from the start location is 1.05meters with a standard deviation of 0.168 meters while SLAM has anaverage of 0.58 meters and a standard deviation of 0.286 meters. Sincethe standard deviation for Dead Reckoning is small, it shows that it isconsistently off, while SLAM has a relatively high standard deviation,meaning that although it can be really accurate, it is not precise allthe time. This shows that SLAM is 180% better at keeping the skiddingmoderated and ending up in the same place as it started on the map.Since this test has the robot skidding the entire time, and all of theskidding occurs during a clockwise turn, all of the errors are beingaccumulated the fastest out of all the tests. SLAM's ability to uselaser scans to correct its position is more apparent in this test.

The Straight Wall Test:

This test sees how well the robot keeps its odometry together as itskids in opposite directions. For SLAM, only one distinct wall isproduced on the map. On the Dead Reckoning map, there are many wallsvisualized, but for the sake of analyzing, we could be just looking atthe actual wall and the wall farthest from the actual wall. By comparingthe farthest wall to the actual wall (which can be done by placing aline between the end and beginning of the robot's path, where it wasagainst the wall), we get the accuracy of the generated wall. This testhad the skidding in opposite directions. In the case of Dead Reckoning,it does not show one inaccurate map, but multiple walls placed atop eachother. SLAM manages to keep the robot's position accurate, preventingmultiple walls from being displayed. For each map, an average of thedistance between the displayed wall and the actual wall was recorded,and overall, the average of those averages for the Dead Reckoning mapare 1.07 meters away from the wall with a standard deviation of 0.308meters, while SLAM has an average of 0.187 meters away with a standarddeviation of 0.035 meters. Since the standard deviation for both ofthese figures are small, it shows that these values are consistentlyaway for the Dead Reckoning and consistently good for SLAM. This showsthat SLAM is 572% better at keeping the wall where it should be when itis put into a constant skidding environment.

This work is unique from other published findings because unlike othermap evaluation methods, this project compares each part of mappingindependently, while other methods compare the map in whole. Our resultswere also unique in that they quantitatively proved a fatal flaw inSLAM, allowing for more improvement of the SLAM algorithm byunderstanding which portions of the algorithm needs improvement, and inturn, allows programmers to be more direct in fixing problems that showup.

This project also allows for less strict testing environments, whereasbefore, a carefully and tediously measured map of the room being testedin has to be made. On the contrary, we have now created a standardtesting environment for map-making algorithms, which can easily bereconstructed. In almost any environment, one can slightly change theirperimeters to match the standard testing area and complete all fivetests, and evaluated their own algorithms.

After conducting this project, one can see that SimultaneousLocalization and Mapping, or SLAM, is a more effective method of mappingthan Dead Reckoning. Using laser scans to correct wheel encoder datawhen wheels slip and skid is an efficient way to make a more accuratemap. In four out of the five tests conducted to evaluate map accuracy,SLAM performed better and displayed a better map 4 out of the 5 times.The reason SLAM did not accurately display the 90 Degrees Twice Test isbecause in a rapidly changing environment, SLAM's laser data incorrectlyfixes the encoder data, creating two different images of one object.This is the one major error in SLAM, which could be assessed in a futureproject. These results support the hypothesis because they do prove thatSLAM is a better method of mapping than Dead Reckoning. With ROS andSLAM technology, banking automation could be made easier and morehelpful to humans.

The invention claimed is:
 1. A method for conducting banking automationcomprising robotically assisting a plurality of individuals using arobot in retrieval and storage of goods deposited in a bank, whereinduring said retrieval and storage of goods by the robot: (a) each of theplurality of the customers maintains privacy by being placed in aprivate location, (b) the plurality of the customers are able toretrieve and store goods simultaneously, and (c) identification of eachof the plurality of the individuals is verified biometrically, furthercomprising mapping at least a portion of the bank.
 2. The method ofclaim 1, wherein the robot comprises a plurality of robots.
 3. Themethod of claim 1, wherein the mapping is done by the robot.
 4. Themethod of claim 3, wherein the mapping includes determining a shortestdistance between one or more of the plurality of individuals and one ormore locations of the goods.
 5. The method of claim 1, wherein each ofthe one or more locations of the goods has an associated code and eachof the plurality of the individuals has an associated key, and only whenthe robot associates the associated code with the associated key doesthe robot retrieve goods from the one or more locations.
 6. The methodof claim 1, wherein the robot uses a simultaneous localization andmapping (SLAM) technique for odometry.
 7. A banking automation systemcomprising a robot for robotically assisting a plurality of individualsin retrieval and storage of goods deposited in a bank, wherein duringsaid retrieval and storage of goods by the robot: (a) each of theplurality of the individuals maintains privacy by being placed in aprivate location, (b) the plurality of the individuals are able toretrieve and store goods simultaneously, and (c) identification of eachof the plurality of the individuals is verified biometrically, whereineach of one or more location of the goods has an associated code andeach of the plurality of the individuals has an associated key, and onlywhen the robot associates the associated code with the associated keydoes the robot retrieve goods from the one or more locations.
 8. Thesystem of claim 7, wherein the robot comprises a plurality of robots. 9.The system of claim 7, wherein the robot is configured to map at least aportion of the bank.
 10. The system of claim 9, wherein the robotdetermines a shortest distance between one or more of the plurality ofindividuals and one or more locations of the goods.
 11. The system ofclaim 7, wherein the robot uses a simultaneous localization and mapping(SLAM) technique for odometry.
 12. The system of claim 7, wherein thebank comprises a financial institution that safeguards valuables and/ormoney of customers, a tissue bank, a gene bank, a sperm bank, a stemcell bank, a library for storage of books, movies or photographs, acentral intelligence agency that safeguards classified or non-classifieddata and goods, an archive, an armory for storage of weapons, a storagefacility or combinations thereof.
 13. A tangible non-transitory computerreadable medium comprising computer executable instructions that, whenexecuted by one or more processors, cause the one or more processors toconduct banking automation, comprising robotically assisting a pluralityof individuals using a robot in retrieval and storage of goods depositedin a bank, wherein during said retrieval and storage of goods by therobot: (a) each of the plurality of the individuals maintains privacy bybeing placed in a private location, (b) the plurality of the individualsare able to retrieve and store goods simultaneously, and (c)identification of each of the plurality of the individuals is verifiedbiometrically, wherein the robot uses a simultaneous localization andmapping (SLAM) technique for odometry.
 14. The tangible non-transitorycomputer readable medium of claim 13, wherein the robot comprises aplurality of robots.
 15. The tangible non-transitory computer readablemedium of claim 13, further comprising mapping at least a portion of themap.
 16. The tangible non-transitory computer readable medium of claim15, wherein the mapping includes determining a shortest distance betweenone or more of the plurality of individuals and one or more locations ofthe goods.
 17. The tangible non-transitory computer readable medium ofclaim 13, wherein each of one or more locations of the goods has anassociated code and each of the plurality of the individuals has anassociated key, and only when the robot associates the associated codewith the associated key does the robot retrieve goods from the one ormore locations.